An expert-driven digital platform for decision support in sustainable building retrofitting

• Developed an expert-driven digital platform for sustainable building retrofit decision-making. • Supports both global and property-specific configurations with adaptable decision criteria. • Integrates a structured three-step decision-making workflow. • Combines subjective and objective weighting with hybrid analysis for transparent rankings. • Validated through a mid-terrace case study using expert input and location-specific environmental data. This study introduces an expert-guided decision-support platform developed to improve the selection of building retrofit measures for energy efficiency. The platform addresses a key gap in existing tools by combining expert input with systematic decision logic, offering a more transparent and adaptable approach to retrofit planning. Unlike simulation-based or policy-orientated systems, this platform focuses on supporting real-world decisions at the property level. It allows for both general (global) and building-specific configurations, giving users the flexibility to define and adjust decision criteria based on retrofit needs. A three-phase analytical workflow supports users via assigning expert importance, classifying assessment criteria, and deciding on a ranking method. The strategy combines data-driven and expert-based weighing methodologies, resulting in balanced and context-aware outputs. The system includes an explainable AI module that generates editable reasons for final recommendations, allowing stakeholders to better understand and discuss decisions. The platform’s efficacy was demonstrated through a case study of a mid-terrace house, showing strong potential for supporting consistent, stakeholder-informed, and auditable retrofit decisions. This work contributes a flexible and scalable solution of practical value to planners, housing authorities, and retrofit consultants.

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Institutions

Publication Details

Journal
Energy and Buildings
Published
2025-11-28
DOI
https://doi.org/10.1016/j.enbuild.2025.116770
Citations
1
Primary Topic
Building Energy and Comfort Optimization
Type
article
Field-Weighted Citation Impact
1.01
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article

An expert-driven digital platform for decision support in sustainable building retrofitting

Hafiz Muhammad Athar Farid, Vladimir Šimić, Richard Hill, Hafiz Muhammad Shakeel et al.
1 citations
Energy and Buildings
Building Energy and Comfort Optimization
1.01
article

An expert-driven digital platform for decision support in sustainable building retrofitting

Hafiz Muhammad Athar Farid, Vladimir Šimić, Richard Hill, Hafiz Muhammad Shakeel, Shamaila Iram
article en
1 citations

Abstract

• Developed an expert-driven digital platform for sustainable building retrofit decision-making. • Supports both global and property-specific configurations with adaptable decision criteria. • Integrates a structured three-step decision-making workflow. • Combines subjective and objective weighting with hybrid analysis for transparent rankings. • Validated through a mid-terrace case study using expert input and location-specific environmental data. This study introduces an expert-guided decision-support platform developed to improve the selection of building retrofit measures for energy efficiency. The platform addresses a key gap in existing tools by combining expert input with systematic decision logic, offering a more transparent and adaptable approach to retrofit planning. Unlike simulation-based or policy-orientated systems, this platform focuses on supporting real-world decisions at the property level. It allows for both general (global) and building-specific configurations, giving users the flexibility to define and adjust decision criteria based on retrofit needs. A three-phase analytical workflow supports users via assigning expert importance, classifying assessment criteria, and deciding on a ranking method. The strategy combines data-driven and expert-based weighing methodologies, resulting in balanced and context-aware outputs. The system includes an explainable AI module that generates editable reasons for final recommendations, allowing stakeholders to better understand and discuss decisions. The platform’s efficacy was demonstrated through a case study of a mid-terrace house, showing strong potential for supporting consistent, stakeholder-informed, and auditable retrofit decisions. This work contributes a flexible and scalable solution of practical value to planners, housing authorities, and retrofit consultants.

Energy and BuildingsVol. 352
Doğuş University (TR), University of Huddersfield (GB), Széchenyi István University (HU), Leeds Beckett University (GB), Yuan Ze University (TW)
Openalex Percentile: Top 18%
Building Energy and Comfort Optimization
1.01
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